Open AccessOutlier Detection in IoT Using Generative Adversarial Network
Author(s) -
B. Joyce Beula Rani,
L. Sumathi
Publication year - 2020
Publication title -
international journal of scientific research in computer science, engineering and information technology
Language(s) - English
Resource type - Journals
ISSN - 2456-3307
DOI - 10.32628/cseit206452
Subject(s) - computer science , anomaly detection , adversarial system , internet of things , task (project management) , outlier , key (lock) , generative grammar , blocking (statistics) , artificial intelligence , scale (ratio) , computer security , machine learning , data mining , computer network , engineering , physics , systems engineering , quantum mechanics
Usage of IoT products have been rapidly increased in past few years. The large number of insecure Internet of Things (IoT) devices with low computation power makes them an easy and attractive target for attackers seeking to compromise these devices and use them to create large-scale attacks. Detecting those attacks is a time consuming task and it needs to be identified shortly since it keeps on spreading. Various detection methods are used for detecting these attacks but attack mechanism keeps on evolving so a new detection approach must be introduced to detect their presence and thus blocking their spreading. In this paper a deep learning approach called GAN – Generative Adversarial Network can be used to detect this outlier and achieve 85% accuracy.